The Time-to-Action Dilemma



dreamstime_m_26639042If you can’t answer these 3 questions in less than 10 minute
s
(and I suspect that you can’t), then your supply chain is not the lever it could be to
 drive more revenue with better margin and less working capital:
1) What are inventory turns by product category (e.g. finished goods, WIP, raw materials, ABC category, etc.)?  How are they trending?  Why?
2) What is the inventory coverageWhat will projected inventory be at by the start of a promotion or season.  Within sourcing, manufacturing or distribution constraints, what options do I have if my demand spikes or tanks?
3) What proportion (and how many) of your customer orders (or margin or revenue) shipped at 99% on-time and in-full?  How many at 98%? And so on . . . Do you understand the drivers?

The slack time that global competition is allowing you to have between planning and execution is collapsing at an accelerating rate.

You need to know the “What?” and the “Why? so you can determine what to do before it’s too late.  

You need to answer the questions that your ERP and APS can’t so your supply chain makes your business more valuable.

Since supply chain decisions are all about managing interrelated goals and trade-offs, data may need to come from various ERP systems, OMS, APS, WMS, MES, and more, so unless you have a platform that consolidates and blends data from end-to-end at every level of granularity and along all dimensions, you will always be reinventing the wheel when it comes to finding and collecting the data for decision support.  It will always take too long.  It will always be too late.

You need the kind of platform that will deliver diagnostic insights so that you can know not just what, but why.  And, once you know what is happening and why, you need to know what to do — your next best action, or at least viable options and their risks . . . and you need that information in context and “in the moment”.

In short, you need to detect opportunities and challenges in your execution and decision-making, diagnose the causes, and direct the next best action in a way that brings execution and decision-making together.

If you don’t have all three now – Detect, Diagnose and Direct – in a way that covers your end-to-end value network, you need to explore how you can get there.

As we approach the weekend, I’ll leave you with this thought to ponder:  Leadership comes from a commitment to something greater than yourself that compels maximum contribution, whether that is leading, following, or just getting out of the way.”

What are “Analytics”?

“Analytics” is one of those business buzz words formed by transforming an adjective into a noun. 

So forceful and habitual is such misuse of language that one might call it a compulsion among business analysts and writers.

The term “analytics” commonly refers to software tools that can be used to organize, report, and sometimes visualize data in attempt to lend meaning for decision-makers.  These capabilities have been advanced in recent years so that many types of graphical displays can be readily employed to expose data and try to make information from it.   “Analytics” has been used to refer to a very broad array of software applications.  Numerous industry analysts have attempted to segment these applications in various ways.  “Analytics” refers to so many kinds of applications that it is useful to establish some broad categories.

A simple, though imperfect, scheme such as the following may be the most useful where the potential value that can be achieved through each category increases from #1 through #4.

Reports – repetitively run displays of pre-aggregated and sorted information with limited or no user interactivity.

Dashboards – frequently updated displays of performance metrics which can be displayed graphically.  They are ideally tailored to the needs of a given role.  Dashboards support the measurement of performance, based on pre-aggregated data with some user selection and drill-down capability.  Hierarchies of metrics have been created that attempt to facilitate a correlation between responsibility and performance indicators.  The most common such model is the Supply Chain Operations Reference Model (SCOR Model) that was created and is maintained by the Supply Chain Council.

Data Analysis Tools – interactive software applications that enable data analysts to dynamically aggregate, sort, plot, and otherwise explore data, based on metadata.  Significant advancements have been made in recent years to dramatically expand the options for visualizing data and accelerating the speed at which these tools can generate results.

Decision Support/Management Science Tools – simulation, optimization, and other approaches to multi-criteria decisions which require the application of statistics and mathematical modeling and solving.

Let’s focus on Decision Support/Management Science Tools, the category with the most potential for adding value to strategic (high value) decision-making in a sustained fashion. 

So, then, if that is what analytics are, do they enable higher quality decisions in less time, and if so, to what extent are those better decisions in less time driving cash flow and value for their business?  These are critically important questions because improved, integrated decision-making that is based in facts and adjusted for risk drives the bottom line.

Execution is good, but operational execution under a poor decision set is like going fast in the wrong direction.  It is bad, but perhaps not immediately fatal.  Poor decisions will put a business under very quickly.

Enabling higher quality decisions in less time depends on the decision-maker, but it can also depend on the tools employed and the skills of the analysts using the tools. 

The main activities in using these tools involve the following:

  1. Sifting through the oceans of data that exist in today’s corporate information systems
  2. Synthesizing the relevant data into information (a thoughtful data model within an analytical application is helpful, but not sufficient)
  3. Presenting it in such a way so that a responsible manager can combine it with experience and quickly know how to make a better decision

Obtaining a valuable result requires careful preparation and skilled interaction, asking the right questions initially and throughout the above activities.

Some of the questions that need to be asked before the data can be synthesized into information in a useful way are represented by those given below:

  1. What is the business goal?
  2. What decisions are required to reach the goal?
  3. What are the upper and lower bounds of each decision? (Which outcomes are unlivable?)
  4. How sensitive is one decision to the outcome of other, interdependent decisions?
  5. What risks are associated with a given decision outcome?
  6. Will a given decision today impact the options for the same decision tomorrow?
  7. What assumptions are implicitly driven by insufficient data?
  8. How reliable is the data upon which the decision is based?
    • Is it accurate?
    • How much of the data has been driven by one-time events that are not repeatable?
    • What data is missing?
    • Is the data at the right level of detail?
    • How might the real environment in which the decision is to be implemented be different from that implied by the data and model (i.e. an abstraction of reality)?
    • How can the differences between reality and its abstraction be reconciled so that the results of the model are useful?

Ask the right questions.

Know the relative importance of each.

Understand which techniques to apply in order to prioritize, analyze and synthesize the data into useful information that enables faster, better decisions.

We often think of change when a new calendar year rolls around.  Since this is my first post of the new year, I”ll leave you with one of my favorite quotes on change.  Leo Tolstoy:  “Everybody thinks of changing humanity, and nobody thinks of changing himself.”

Have a wonderful weekend!

Uncovering Unprofitable Decisions

Last week, I expanded on the concept of a profitability profile, the first point of three that I highlighted here.   This week, as promised, I want to expend a few words on analyzing decision processes that are hurting profitability because they could be improved. 

Execution is important in business, but knowing what to execute is even more fundamental and critical, at all levels from strategic to tactical.

You can make better business decisions (some of them are called planning – demand planning, supply planning, capacity planning, transportation planning) in less time if you embed the decision points in a process and support the process with necessary analytics (see “Finding the Value in Your Value Network“).

Decision processes need to be integrated because many of them are interdependent.  If made in isolation, reduced profit is almost guaranteed.  This has driven a continuing interest in Sales and Operations Planning, the goal of which is to include the relevant data, perspectives and analytics so that key decisions about running the business are coordinated and optimal for the business as a whole.  Sometimes, this process is called Integrated Business Planning or Integrated Business Management, or perhaps other titles.  The important thing is not the moniker, but the fundamentals of getting the process right for your business. 

Business decisions that frequently lead to reduced profit can include pricing, new product development, supply chain network design, demand planning, capacity planning, inventory planning, production scheduling, and others.  One big reason these decision processes can fail is that they are not sufficiently linked, leaving blind spots that keep relevant tradeoffs from being considered.

Finally, just because you have spent lots of money licensing, implementing and supporting software applications that were supposed to address specific decisions or even decision processes, doesn’t mean you are any better off.  (You already know this!)  There are lots of reasons, including the following:

  1. The software didn’t fit the business challenge
  2. The data model doesn’t effectively abstract reality
  3. Your master data is inadequately maintained
  4. The planner or analyst doesn’t have sufficient experience or skill to deal with data model deficiencies and master data defects
  5. The software application doesn’t allow the skilled planner or analyst to interact with it so that they can compensate for data model deficiencies and master data defects
  6. The parameters of the software application aren’t correctly set
  7. The software application leaves out important tradeoffs
  8. And the list can go on . . .

Remember that rapid execution is only as good as the plan to which you are executing, and that’s the thought for this weekend.

Thanks again for stopping by and have a wonderful weekend!

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